Batch Policy Learning in Average Reward Markov Decision Processes

We consider the batch (off-line) policy learning problem in the infinite horizon Markov Decision Process. Motivated by mobile health applications, we focus on learning a policy that maximizes the long-term average reward. We propose a doubly robust estimator for the average reward and show that it achieves semiparametric efficiency given multiple trajectories collected under some behavior policy. Based on the proposed estimator, we develop an optimization algorithm to compute the optimal policy in a parameterized stochastic policy class. The performance of the estimated policy is measured by the difference between the optimal average reward in the policy class and the average reward of the estimated policy and we establish a finite-sample regret guarantee. To the best of our knowledge, this is the first regret bound for batch policy learning in the infinite time horizon setting. The performance of the method is illustrated by simulation studies.